helpmefindaname commited on
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create model

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  1. README.md +51 -0
  2. pytorch_model.bin +3 -0
README.md ADDED
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+ ---
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+ tags:
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+ - flair
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+ - entity-mention-linker
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+ ---
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+
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+ ## diseases-exact-match
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+
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+ Biomedical Entity Mention Linking for diseases
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+
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+ ### Demo: How to use in Flair
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+
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+ Requires:
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+ - **[Flair](https://github.com/flairNLP/flair/)>=0.14.0** (`pip install flair` or `pip install git+https://github.com/flairNLP/flair.git`)
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+
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+
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+ ```python
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+ from flair.data import Sentence
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+ from flair.models import Classifier, EntityMentionLinker
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+
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+ sentence = Sentence("Behavioral abnormalities in the Fmr1 KO2 Mouse Model of Fragile X Syndrome")
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+
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+ # load hunflair to detect the entity mentions we want to link.
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+ tagger = Classifier.load("hunflair")
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+ tagger.predict(sentence)
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+
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+ # load the linker and dictionary
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+ linker = EntityMentionLinker.load("helpmefindaname/flair-eml-diseases-exact-match")
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+ dictionary = linker.dictionary
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+
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+ # find then candidates for the mentions
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+ linker.predict(sentence)
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+
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+ # print the results for each entity mention:
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+ for span in sentence.get_spans(linker.entity_label_type):
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+ print(f"Span: {span.text}")
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+ for candidate_label in span.get_labels(linker.label_type):
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+ candidate = dictionary[candidate_label.value]
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+ print(f"Candidate: {candidate.concept_name}")
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+ ```
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+
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+
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+ As an alternative to downloading the already precomputed model (much storage). You can also build the model
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+ and compute the embeddings for the dataset using:
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+ ```python
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+ linker = EntityMentionLinker.build("exact-string-match", "diseases", dictionary_name_or_path="ctd-diseases", hybrid_search=False, entity_type="diseases-eml")
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+ ```
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+ This will reduce the download requirements, at the cost of computation.
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+
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+ This EntityMentionLinker uses [https://huggingface.co/exact-string-match](exact-string-match) as embeddings for linking mentions to candidates.
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+
pytorch_model.bin ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:0bc10fe044bbb3197c73f919ccb9517587163aca3ba13ff7e0e16d92b5f3bea6
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+ size 2068348